QuMAB models each annotator with a lightweight query in a cross-attention network, reconstructs missing labels, and reports accuracy gains over aggregation baselines on two new dense-label datasets.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.MM 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
QuMAB: Query-based Multi-Annotator Behavior Modeling with Reliability under Sparse Labels
QuMAB models each annotator with a lightweight query in a cross-attention network, reconstructs missing labels, and reports accuracy gains over aggregation baselines on two new dense-label datasets.